Surrogate prediction of the breakthrough curve of solute transport in rivers using its reach length dependence.

Techniques for predicting the contaminant cloud propagation along a stream are necessary for swift action against contaminant spill accidents in fluvial systems. Due to their low computational cost, one-dimensional solute transport models have conventionally been employed, in which the complex channel characteristics are considered using model parameters. However, the determination of such parameters relies predominantly on optimization techniques based on pre-measured tracer data, which are usually unavailable for unexpected accidents. The present paper suggests an alternative method for predicting a breakthrough curve (BTC) variation along an unmeasured stream reach where no flow information is provided. In this study, we investigated the relationship between directly-measured flow properties and BTC characteristics based on field tracer experiments. Using statistical features of the tracer BTCs, we devised a regressive prediction method for estimating the BTC features as a function of travel distance, and validated the method by comparison with simulations using both a one-dimensional advection-dispersion equation (ADE) and transient storage model (TSM), whose parameters were calibrated at upstream reaches. The proposed regressive predictions were relatively accurate than those from parameter-calibrated models, and this advantage was more apparent for long-distance predictions for the unmeasured river reach.

Hyperspectral retrievals of suspended sediment using cluster-based machine learning regression in shallow waters.

Remote sensing of suspended sediment in shallow waters is challenging because of the increased optical variability of the water, resulting from the influence of suspended matter in the water column and the heterogeneous bottom properties. To overcome this limitation, in this study, we developed a novel framework called cluster-based machine learning regression for optical variability (CMR-OV), using the Gaussian mixture model (GMM) clustering technique and a random forest regressor (RFR). We evaluated the model using an optically complex dataset from a field-scale experiment. This experiment was conducted with four sediment types injected into an experimental meandering channel divided into two reaches with submerged vegetation and a natural sand bottom. We obtained high-resolution hyperspectral images using unmanned aerial vehicles (UAVs) and measured the in situ suspended sediment concentration using laser diffraction sensors. Based on optical similarity, we used CMR-OV to divide the hyperspectral dataset into several clusters. Then, we built separate RFR models for each cluster using the corresponding spectral bands that were selected using recursive feature elimination (RFE). Thus, we found that the proposed CMR-OV yielded superior results compared to the conventional RFR model, decreasing the total error score by 10.81%. The optical spectral bands of each cluster were distinguished from each other, indicating that the datasets that were spectrally discriminated from clustering enhanced the performance of the estimator. By comparing the clustered spectral dataset and physical factors, we proved the bottom type was the most critical factor in separating the clusters, even though the variability in the sediment properties also induced substantial spectral changes. Our findings demonstrated that CMR-OV accurately reproduced the spatiotemporal distribution of suspended sediment under optically complex conditions by addressing the heterogeneity of bottom reflectance in shallow water.

Atomic layer deposition for rutile structure TiO2thin films using a SnO2seed layer and low temperature heat treatment.

We study the rutile-TiO2film deposition with a high-kvalue using a SnO2seed layer and a low temperature heat treatment. Generally, heat treatment over 600 °C is required to obtain the rutile-TiO2film. However, By using a SnO2seed layer, we obtained rutile-TiO2films with heat treatments as low as 400 °C. The XPS analysis confirms that the SnO2and TiO2film were deposited. The XRD analysis showed that a heat treatment at 400 °C after depositing the SnO2and TiO2films was effective in obtaining the rutile-TiO2film when the SnO2film was thicker than 10 nm. The TEM/EDX analysis show that no diffusion in the thin film between TiO2and SnO2. The dielectric constant of the TiO2film deposited on the SnO2film (20 nm) was 67, which was more than twice as high as anatase TiO2dielectric constant (Anatase TiO2dielectric constant : 15-40). The current density was 10-4A cm-2at 0.7 V and this value confirmed that the leakage current was not affected by the SnO2seed layer.

The effect of an annealing process on atomic layer deposited TiO2thin films.

In this paper, we study the property changes in TiO2thin films related to annealing under various conditions. XPS analysis showed that the concentration of oxygen vacancies in TiO2thin films was reduced by annealing. In the case of annealing in an O2and air atmosphere, the oxygen vacancy concentration was reduced to the greatest extent as oxygen diffused into the TiO2thin film and rearrangement of atoms occurred. XRD analysis showed that the anatase structure of annealed TiO2thin films was clearly present compared to the as-deposited TiO2thin film.I-Vanalysis showed that the lower the concentration of oxygen vacancy, the lower the leakage current (O2annealed TiO2: 10-4A cm-2) than as dep TiO2thin film (∼10-1A cm-2). The dielectric constant of annealed TiO2thin films was 26-30 which was higher than the as-deposited TiO2thin film (k âˆ¼ 18) because the anatase structure became more apparent.